Least Squares Support Vector Machines for Kernel CCA in Nonlinear State-Space Identification

@inproceedings{Verdult2004LeastSS,
  title={Least Squares Support Vector Machines for Kernel CCA in Nonlinear State-Space Identification},
  author={Vincent Verdult and Johan A. K. Suykens and Jeroen Boets and Ivan Goethals and Bart De Moor},
  year={2004}
}
We show that kernel canonical correlation analysis (KCCA) can be used to construct a state sequence of an unknown nonlinear dynamical system from delay vectors of inputs and outputs. In KCCA a feature map transforms the available data into a high dimensional feature space, where classical CCA is applied to find linear relations. The feature map is only implicitly defined through the choice of a kernel function. Using a least squares support vector machine (LS-SVM) approach an appropriate form… CONTINUE READING
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